code: def setUpClass(cls):
      super(PipelineTest, cls).setUpClass()
  
      # create an artificial training dataset of two features with labels computed from known weights
      np.random.seed(1234)
      cls.features = np.random.rand(1000, 2)
      cls.weights = np.array([3.14, 1.618])
      cls.labels = np.matmul(cls.features, cls.weights)
      # convert to Python types for use with Spark DataFrames
      cls.train_examples = [
          (cls.features[i].tolist(), [cls.labels[i].item()])
          for i in range(1000)
      ]
      # create a simple test dataset
      cls.test_examples = [([1.0, 1.0], [0.0])]
  
      # define model_dir and export_dir for tests
      cls.model_dir = os.getcwd() + os.sep + "test_model"
      cls.export_dir = os.getcwd() + os.sep + "test_export"
      cls.tfrecord_dir = os.getcwd() + os.sep + "test_tfr"
  
file_path: TensorFlowOnSpark/tests/test_pipeline.py
imports: import numpy
  import os
  import shutil
  import test
  import unittest
  from tensorflowonspark import compat
  from tensorflowonspark.pipeline import HasBatchSize, HasSteps, Namespace, TFEstimator, TFParams
  from tensorflow.keras import Sequential
  from tensorflow.keras.layers import Dense
label_desc: Use image node_id 1 for calling the PipelineTest obj's underlying member method code with example usage: obj.setUpClass(cls) without return types